Organic Electro-Optic Materials with High Electro-Optic Coefficients and Strong Stability.

The preparation of high-performance electro-optical materials is one of the key factors determining the application of optoelectronic communication technology such as 5G communication, radar detection, terahertz, and electro-optic modulators. Organic electro-optic materials have the advantage of a high electro-optic coefficient (~1000 pm/V) and could allow the utilization of photonic devices for the chip-scale integration of electronics and photonics, as compared to inorganic electro-optic materials. However, the application of organic nonlinear optical materials to commercial electro-optic modulators and other fields is also facing technical bottlenecks. Obtaining an organic electro-optic chromophore with a large electro-optic coefficient (r33 value), thermal stability, and long-term stability is still a difficulty in the industry. This brief review summarizes recent great progress and the strategies to obtain high-performance OEO materials with a high electro-optic coefficient and/or strong long-term stability. The configuration of D-π-A structure, the types of materials, and the effects of molecular engineering on the electro-optical coefficient and glass transition temperature of chromophores were summarized in detail. The difficulties and future development trends in the practical application of organic electro-optic materials was also discussed.

State-of-the-art of lignin-derived carbon nanodots: Preparation, properties, and applications.

Lignin-derived carbon nanodots (LCNs) are nanometer-scale carbon spheres fabricated from naturally abundant lignin. Owing to rich and highly heritable graphene like π-π conjugated structure of lignin, to fabricate LCNs from it not only endows LCNs with on-demand tunable size and optical features, but also further broadens the green and chemical engineering of carbon nanodots. Recently, they have become increasingly popular in sensing, bioimaging, catalysis, anti-counterfeiting, energy storage/conversion, and others. Despite the enormous research efforts put into the ongoing development of lignin value-added utilization, few commercial LCNs are available. To have a deeper understanding of this issue, critical impacts on the preparation, properties, and applications of state-of-the-art LCNs are carefully reviewed and discussed. A concise analysis of their unique advantages, limitations for specific applications, and current challenges and outlook is conducted. We hope that this review will stimulate further advances in the functional material-oriented production of lignin.

Highly Efficient and Stable Binary Cross-Linkable/ Self-Assembled Organic Nonlinear Optical Molecular Glasses.

The development of electro-optical materials with high chromophore loading levels that possess ultrahigh electro-optic coefficients and high long term alignment stability is a challenging topic. Anthracene-maleimide Diels-Alder (DA) reaction and π-π interaction of Anthracene-pentafluorobenzene and benzene-pentafluorobenzene are developed for making highly efficient binary cross-linkable/self-assembled dendritic chromophores FZL1-FZL4. A covalently or non-covalently cross-linked network is formed by DA reaction or π-π interaction after electric field poling orientation, which greatly improves the long-term alignment stability of the materials. An electro-optic coefficient up to 266 pm V-1 and glass transition temperature as high as 178 °C are achieved in cross-linked film FZL1/FZL2, and 272-308 pm V-1 is achieved for self-assembled films FZL1/FZL4 and FZL3/FZL4 due to high chromophore density (3.09-4.02 × 1020 molecules cm-3 ). Long-term alignment stability tests show that after heating at 85 °C for over 500 h, 99.73% of the initial r33 value is maintained for poled crosslinked electro-optic films 1:1 FZL1/FZL2. The poled self-assembled electro-optic films 1:1 FZL1/FZL4 and 1:1 FZL3/FZL4 can still maintain more than 97.11% and 98.23%, respectively, of the original electro-optic coefficient after being placed at room temperature for 500 h. The excellent electro-optic coefficient and stability of the material indicate the practical application prospects of organic electro-optic materials.

High-Quality Epitaxial N Doped Graphene on SiC with Tunable Interfacial Interactions via Electron/Ion Bridges for Stable Lithium-Ion Storage.

Tailoring the interfacial interaction in SiC-based anode materials is crucial to the accomplishment of higher energy capacities and longer cycle lives for lithium-ion storage. In this paper, atomic-scale tunable interfacial interaction is achieved by epitaxial growth of high-quality N doped graphene (NG) on SiC (NG@SiC). This well-designed NG@SiC heterojunction demonstrates an intrinsic electric field with intensive interfacial interaction, making it an ideal prototype to thoroughly understand the configurations of electron/ion bridges and the mechanisms of interatomic electron migration. Both density functional theory (DFT) analysis and electrochemical kinetic analysis reveal that these intriguing electron/ion bridges can control and tailor the interfacial interaction via the interfacial coupled chemical bonds, enhancing the interfacial charge transfer kinetics and preventing pulverization/aggregation. As a proof-of-concept study, this well-designed NG@SiC anode shows good reversible capacity (1197.5 mAh g-1 after 200 cycles at 0.1 A g-1) and cycling durability with 76.6% capacity retention at 447.8 mAh g-1 after 1000 cycles at 10.0 A g-1. As expected, the lithium-ion full cell (LiFePO4/C//NG@SiC) shows superior rate capability and cycling stability. This interfacial interaction tailoring strategy via epitaxial growth method provides new opportunities for traditional SiC-based anodes to achieve high-performance lithium-ion storage and beyond.

A novel design of DNA duplex containing programmable sensing sites for nanopore-based length-resolution reading and applications for Pb2+ and cfDNA analysis.

Glass nanopore is an ideal candidate for biosensors due to its unique advantages such as label-free analysis, single-molecule sensitivity, and easy operation. Previous studies have shown that glass nanopores can distinguish different lengths of double-stranded DNA (dsDNA) at the same time with the length-resolution ability. Based on this, we proposed a novel design of a dsDNA block containing a programmable sensing site inside, which can be programmed to respond to different target molecules and cleaved into two smaller DNA blocks. When programming the sensing site with different sequences, for example, programming it as the substrate of GR-5 DNAzyme and CRISPR-Cas12a system, the DNA block could realize Pb2+ and cfDNA detection with the length-resolution ability of the glass nanopore. This strategy achieved a Pb2+ detection range from 0.5 nM to 100 nM, with a detection limit of 0.4 nM, and a BRCA-1 detection range from 1 pM to 10 pM, with a detection limit of 1 pM. The programable sensing site is easy to design and has strong expandability, which gives full play to the advantages of glass nanopore in length-resolution ability for dsDNA, and is expected to become an optional design for biosensing strategy for the glass nanopore as a biosensing platform.

A nanopore counter for highly sensitive evaluation of DNA methylation and its application in in vitro diagnostics.

DNA methylation has been considered an essential epigenetic biomarker for diagnosing various diseases, such as cancer. A simple and sensitive way for DNA methylation level detection is necessary. Inspired by the label-free and ultra-high sensitivity of solid-state nanopores to double-stranded DNA (dsDNA), we proposed a nanopore counter for evaluating DNA methylation by integrating a dual-restriction endonuclease digestion strategy coupled with polymerase chain reaction (PCR) amplification. Simultaneous application of BstUI/HhaI endonucleases can ensure the full digestion of the unmethylated target DNA but shows no effect on the methylated ones. Therefore, only the methylated DNA remains intact and can trigger the subsequent PCR reaction, producing a large quantity of fixed-length PCR amplicons, which can be directly detected through glassy nanopores. By simply counting the event rate of the translocation signals, the concentration of methylated DNA can be determined to range from 1 aM to 0.1 nM, with the detection limit as low as 0.61 aM. Moreover, a 0.01% DNA methylation level was successfully distinguished. The strategy of using the nanopore counter for highly sensitive DNA methylation evaluation would be a low-cost but reliable alternative in the analysis of DNA methylation.

Bare glassy nanopore for length-resolution reading of PCR amplicons from various pathogenic bacteria and viruses.

In this study, it is confirmed that without addition of organic solvent and embedding polymer hydrogel into glass nanopore, bare glass nanopore can faithfully measure various lengths of DNA duplexes from 200 to 3000 base pairs with 200 base pairs resolution, showing well-separated peak amplitudes of blockage currents. Furthermore, motivated by this readout capability of duplex DNA, amplicons from Polymerase Chain Reaction (PCR) amplification are straightforwardly discriminated by bare glassy nanopore without fluorescent labeling. Except simultaneous discrimination of up to 7 different segments of the same lambda genome, various pathogenic bacteria and viruses including SARS-CoV-2 and its mutants in clinical samples can be discriminated at high resolution. Moreover, quantitative measurement of PCR amplicons is obtained with detection range spanning from 0.75 aM to 7.5 pM and detection limit of 7.5 aM, which reveals that bare glass nanopore can faithfully disclose PCR results without any extra labeling.

TADC: A Topic-Aware Dynamic Convolutional Neural Network for Aspect Extraction.

Aspect extraction is one of the key tasks in fine-grained sentiment analysis. This task aims to identify explicit opinion targets from user-generated documents. Currently, the mainstream methods for aspect extraction are built on recurrent neural networks (RNNs), which are difficult to parallelize. To accelerate the training/testing process, convolutional neural network (CNN)-based methods are introduced. However, such models usually utilize the same set of filters to convolve all input documents, and hence, the unique information inherent in each document may not be fully captured. To alleviate this issue, we propose a CNN-based model that employs a set of dynamic filters. Specifically, the proposed model extracts the aspects in a document using the filters generated from the aspect information intrinsic in the document. With the dynamically generated filters, our model is capable of learning more important features concerning aspects, thus promoting the effectiveness of aspect extraction. Furthermore, considering that aspects can be grouped into certain topics that conversely indicate the target words that need to be extracted, we naturally introduce a neural topic model (NTM) and integrate latent topics into the CNN-based module to help identify aspects. Experiments on two benchmark datasets demonstrate that the joint model is able to effectively identify aspects and produce interpretable topics.

Balancing Constraints and Objectives by Considering Problem Types in Constrained Multiobjective Optimization.

Constrained multiobjective optimization problems widely exist in real-world applications. To handle them, the balance between constraints and objectives is crucial, but remains challenging due to non-negligible impacts of problem types. In our context, the problem types refer particularly to those determined by the relationship between the constrained Pareto-optimal front (PF) and the unconstrained PF. Unfortunately, there has been little awareness on how to achieve this balance when faced with different types of problems. In this article, we propose a new constraint handling technique (CHT) by taking into account potential problem types. Specifically, inspired by the prior work, problems are classified into three primary types: 1) I; 2) II; and 3) III, with the constrained PF being made up of the entire, part and none of the unconstrained counterpart, respectively. Clearly, any problem must be one of the three types. For each possible type, there exists a tailored mechanism being used to handle the relationships between constraints and objectives (i.e., constraint priority, objective priority, or the switch between them). It is worth mentioning that exact problem types are not required because we just consider their possibilities in the new CHT. Conceptually, we show that the new CHT can make a tradeoff among different types of problems. This argument is confirmed by experimental studies performed on 38 benchmark problems, whose types are known, and a real-world problem (with unknown types) in search-based software engineering. Results demonstrate that within both decomposition-based and nondecomposition-based frameworks, the new CHT can indeed achieve a good tradeoff among different problem types, being better than several state-of-the-art CHTs.

Fully Complex-Valued Dendritic Neuron Model.

A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal processing. Yet no complex value representations appear in single neuron architectures. In this article, we first extend DNM from a real-value domain to a complex-valued one. Performance of complex-valued DNM (CDNM) is evaluated through a complex XOR problem, a non-minimum phase equalization problem, and a real-world wind prediction task. Also, a comparative analysis on a set of elementary transcendental functions as an activation function is implemented and preparatory experiments are carried out for determining hyperparameters. The experimental results indicate that the proposed CDNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.

Solving Dynamic Traveling Salesman Problems With Deep Reinforcement Learning.

A traveling salesman problem (TSP) is a well-known NP-complete problem. Traditional TSP presumes that the locations of customers and the traveling time among customers are fixed and constant. In real-life cases, however, the traffic conditions and customer requests may change over time. To find the most economic route, the decisions can be made constantly upon the time-point when the salesman completes his service of each customer. This brings in a dynamic version of the traveling salesman problem (DTSP), which takes into account the information of real-time traffic and customer requests. DTSP can be extended to a dynamic pickup and delivery problem (DPDP). In this article, we ameliorate the attention model to make it possible to perceive environmental changes. A deep reinforcement learning algorithm is proposed to solve DTSP and DPDP instances with a size of up to 40 customers in 100 locations. Experiments show that our method can capture the dynamic changes and produce a highly satisfactory solution within a very short time. Compared with other baseline approaches, more than 5% improvements can be observed in many cases.

Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems.

Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by the weight decomposition of objectives. This article proposes a concise meta-learning-based DRL approach. It first trains a meta-model by meta-learning. The meta-model is fine-tuned with a few update steps to derive submodels for the corresponding subproblems. The Pareto front is then built accordingly. Compared with other learning-based methods, our method can greatly shorten the training time of multiple submodels. Due to the rapid and excellent adaptability of the meta-model, more submodels can be derived so as to increase the quality and diversity of the found solutions. The computational experiments on multiobjective traveling salesman problems and multiobjective vehicle routing problems with time windows demonstrate the superiority of our method over most of the learning-based and iteration-based approaches.

Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification.

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

Heterogeneous Interaction Modeling With Reduced Accumulated Error for Multiagent Trajectory Prediction.

Dynamical complex systems composed of interactive heterogeneous agents are prevalent in the world, including urban traffic systems and social networks. Modeling the interactions among agents is the key to understanding and predicting the dynamics of the complex system, e.g., predicting the trajectories of traffic participants in the city. Compared with interaction modeling in homogeneous systems such as pedestrians in a crowded scene, heterogeneous interaction modeling is less explored. Worse still, the error accumulation problem becomes more severe since the interactions are more complex. To tackle the two problems, this article proposes heterogeneous interaction modeling with reduced accumulated error (HIMRAE) for multiagent trajectory prediction. Based on the historical trajectories, our method infers the dynamic interaction graphs among agents, featured by directed interacting relations and interacting effects. A heterogeneous attention mechanism (HAM) is defined on the interaction graphs for aggregating the influence from heterogeneous neighbors to the target agent. To alleviate the error accumulation problem, this article analyzes the error sources from the spatial and temporal perspectives, and proposes to introduce the graph entropy and the mixup training strategy for reducing the two types of errors, respectively. Our method is examined on three real-world datasets containing heterogeneous agents, and the experimental results validate the superiority of our method.

A signal on-off strategy based on the digestion of DNA cubes assisted by the CRISPR-Cas12a system for ultrasensitive HBV detection in solid-state nanopores.

Solid-state nanopores have been proven as a powerful platform for label-free single-molecule analysis. However, due to its relatively low resolution and selectivity, developing biosensors with good translocation signals faces two significant challenges: (1) small-sized chemical or biological targets show difficulty in producing recognizable translocation signals because of their weak interaction with the nanopore and (2) protein interferents that widely exist in biological samples or buffers would considerably deteriorate the noise level of the nanopore, submerging the translocation signal. Herein, we demonstrate an effective way to overcome both the challenges. DNA cubes were used as signal transducers that can achieve an ultra-high (>50 : 1) signal-to-noise ratio (SNR) translocation signal, which is maintained even in protein interferent-rich buffers. A sensing strategy was constructed via hepatitis B virus (HBV) target-triggered cleavage of the component elements of the DNA cube with the assistance of the CRISPR-Cas12a technology, which caused a great drop in the translocation rate. The elements to cleave were optimized, and the sensor performance was tested in different protein stabilizer-rich buffers and human serum. Coupling with the polymerase chain reaction (PCR) pre-amplification technology, HBV-positive or -negative classification was achieved with the detection limit reaching 5 aM. It is worth noting that in our method, all reaction buffers were directly used without further optimization, which is of great help for the practical application of solid-state nanopores.

Smart Approach for the Design of Highly Selective Aptamer-Based Biosensors.

Aptamers are chemically synthesized single-stranded DNA or RNA oligonucleotides widely used nowadays in sensors and nanoscale devices as highly sensitive biorecognition elements. With proper design, aptamers are able to bind to a specific target molecule with high selectivity. To date, the systematic evolution of ligands by exponential enrichment (SELEX) process is employed to isolate aptamers. Nevertheless, this method requires complex and time-consuming procedures. In silico methods comprising machine learning models have been recently proposed to reduce the time and cost of aptamer design. In this work, we present a new in silico approach allowing the generation of highly sensitive and selective RNA aptamers towards a specific target, here represented by ammonium dissolved in water. By using machine learning and bioinformatics tools, a rational design of aptamers is demonstrated. This "smart" SELEX method is experimentally proved by choosing the best five aptamer candidates obtained from the design process and applying them as functional elements in an electrochemical sensor to detect, as the target molecule, ammonium at different concentrations. We observed that the use of five different aptamers leads to a significant difference in the sensor's response. This can be explained by considering the aptamers' conformational change due to their interaction with the target molecule. We studied these conformational changes using a molecular dynamics simulation and suggested a possible explanation of the experimental observations. Finally, electrochemical measurements exposing the same sensors to different molecules were used to confirm the high selectivity of the designed aptamers. The proposed in silico SELEX approach can potentially reduce the cost and the time needed to identify the aptamers and potentially be applied to any target molecule.

Detection of small-sized DNA fragments in a glassy nanopore by utilization of CRISPR-Cas12a as a converter system.

The fabrication of nanopores with a matched pore size, and the existence of multiple interferents make the reproducible detection of small-sized molecules by means of solid-state nanopores still challenging. A useful method to solve these problems is based on the detection of large DNA nanostructures related to the existence of small-sized targets. In particular, a DNA tetrahedron with a well-defined 3D nanostructure is the ideal candidate for use as a signal transducer. Here, we demonstrate the detection of an L1-encoding gene of HPV18 as a test DNA target sequence in a reaction buffer solution, where long single-stranded DNA linking DNA tetrahedra onto the surface of the magnetic beads is cleaved by a target DNA-activated CRISPR-cas12 system. The DNA tetrahedra are subsequently released and can be detected by the current pulse in a glassy nanopore. This approach has several advantages: (1) one signal transducer can be used to detect different targets; (2) a glassy nanopore with a pore size much larger than the target DNA fragment can boost the tolerance of the contaminants and interferents which often degrade the performance of a nanopore sensor.

TACN: A Topical Adversarial Capsule Network for textual network embedding.

Combining topological information and attributed information of nodes in networks effectively is a valuable task in network embedding. Nevertheless, many prior network embedding methods regarded attributed information of nodes as simple attribute sets or ignored them totally. In some scenarios, the hidden information contained in vertex attributes are essential to network embedding. For instance, networks that contain vertexes with text information play an increasingly important role in our life, including citation networks, social networks, and entry networks. In these textual networks, the latent topic relevance information of different vertexes contained in textual attributes information are valuable in the network analysis process. Shared latent topics of nodes in networks may influence the interaction between them, which is critical to network embedding. However, much prior work for textual network embedding only regarded the text information as simple word sets while ignored the embedded topic information. In this paper, we develop a model named Topical Adversarial Capsule Network (TACN) for textual network embedding, which extracts a low-dimensional latent space of the original network from node structures, vertex attributes, and topic information contained in text of nodes. The proposed TACN contains three parts. The first part is an embedding model, which extracts the embedding representation from the topological structure, vertex attributes, and document-topic distributions. To ensure a consistent training process by back-propagation, we generate document-topic distributions by the neural topic model with Gaussian Softmax constructions. The second part is a prediction model, which is used to exploit labels of vertices. In the third part, an adversarial capsule model is used to help distinguish the latent representations from node structure domain, vertex attribute domain, or document-topic distribution domain. The latent representations, which may come from the three domains, are the output of the embedding model. We incorporate the adversarial idea into the adversarial capsule model to combine the information from these three domains, rather than to distinguish the representations conventionally. Experiments on seven real-world datasets validate the effectiveness of our method.

Nanochannel sensor for sensitive and selective adamantanamine detection based on host-guest competition.

The abuse of adamantanamine (ADA) and its derivatives as veterinary drugs in the poultry industry could cause severe health problems for humans. It is of great need to develop a rapid, cheap and ultrasensitive method for ADA detection. In this study, a sensitive conical nanochannel sensor was established for the rapid quantitative detection of ADA with the distinctive design of the host-guest competition. The sensor was constructed by functionalizing the nanochannel surface with p-toluidine and was then assembled with Cucurbit [7]uril (CB [7]). When ADA is added, it could occupy the cavity of CB [7] due to the host-guest competition and makes CB [7] to release from the CB [7]-p-toluidine complex, resulting in a distinct change of hydrophobicity of the nanochannel, which could be determined by the ionic current. Under the optimal conditions, the strategy permitted sensitive detection of ADA in a linear range of 10-1000 nM. The nanochannel based ADA sensing platform showed both high sensitivity and excellent reproducibility and the limit of detection was 4.54 nM. For the first time, the rapid and sensitive recognition of an illegal medicine was realized based on the host-guest competition method with the nanochannel system and the principle and feasibility of this method were described at length. This strategy provides a simple, reliable, and effective way to apply host-guest system in the development of nanochannel sensor for small-molecule drug detection.

CuO@CoFe Layered Double Hydroxide Core-Shell Heterostructure as an Efficient Water Oxidation Electrocatalyst under Mild Alkaline Conditions.

Designing hierarchical electrocatalysts with superior water oxidation performance is highly desirable for the production of renewable chemical fuels. Here, we report the development of a CuO@CoFe layered double hydroxide core-shell heterostructure supported on Cu foil (CuO@CoFe-LDH/CF) as a highly active catalyst electrode for water oxidation under mild alkaline conditions. In a 0.2 M carbonate buffer solution (pH 11), it only needs a small overpotential of 213 mV to achieve 10 mA cm-2, outperforming all reported electrocatalysts at comparable testing conditions. It also shows outstanding long-term durability.